Merge 8379188 into 447a398

README.md

@@ -159,6 +159,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 - [x] [K-Net (NeurIPS'2021)](configs/knet)
 - [x] [MaskFormer (NeurIPS'2021)](configs/maskformer)
 - [x] [Mask2Former (CVPR'2022)](configs/mask2former)
+- [x] [PIDNet (ArXiv'2022)](configs/pidnet)
 
 </details>
 

README_zh-CN.md

@@ -140,6 +140,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [K-Net (NeurIPS'2021)](configs/knet)
 - [x] [MaskFormer (NeurIPS'2021)](configs/maskformer)
 - [x] [Mask2Former (CVPR'2022)](configs/mask2former)
+- [x] [PIDNet (ArXiv'2022)](configs/pidnet)
 
 </details>
 

configs/pidnet/README.md

@@ -0,0 +1,50 @@
+# PIDNet
+
+> [PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller](https://arxiv.org/pdf/2206.02066.pdf)
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+<a href="https://github.com/XuJiacong/PIDNet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/pidnet.py">Code Snippet</a>
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+Two-branch network architecture has shown its efficiency and effectiveness for real-time semantic segmentation tasks. However, direct fusion of low-level details and high-level semantics will lead to a phenomenon that the detailed features are easily overwhelmed by surrounding contextual information, namely overshoot in this paper, which limits the improvement of the accuracy of existed two-branch models. In this paper, we bridge a connection between Convolutional Neural Network (CNN) and Proportional-IntegralDerivative (PID) controller and reveal that the two-branch network is nothing but a Proportional-Integral (PI) controller, which inherently suffers from the similar overshoot issue. To alleviate this issue, we propose a novel threebranch network architecture: PIDNet, which possesses three branches to parse the detailed, context and boundary information (derivative of semantics), respectively, and employs boundary attention to guide the fusion of detailed and context branches in final stage. The family of PIDNets achieve the best trade-off between inference speed and accuracy and their test accuracy surpasses all the existed models with similar inference speed on Cityscapes, CamVid and COCO-Stuff datasets. Especially, PIDNet-S achieves 78.6% mIOU with inference speed of 93.2 FPS on Cityscapes test set and 80.1% mIOU with speed of 153.7 FPS on CamVid test set.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://raw.githubusercontent.com/XuJiacong/PIDNet/main/figs/pidnet.jpg" width="800"/>
+</div>
+
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------- | -------- | -------------- | ----- | ------------- | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PIDNet | PIDNet-S | 1024x1024 | 120000  | 3.38     | 80.82          | 78.74 | 80.87         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700-bb8e3bcc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700.json) |
+| PIDNet | PIDNet-M | 1024x1024 | 120000  | 5.14     | 71.98          | 80.22 | 82.05         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452-f9bcdbf3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452.json) |
+| PIDNet | PIDNet-L | 1024x1024 | 120000  | 5.83     | 60.06          | 80.89 | 82.37         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514-0783ca6b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514.json) |
+
+## Notes
+
+The pretrained weights in config files are converted from [the official repo](https://github.com/XuJiacong/PIDNet#models).
+
+## Citation
+
+```bibtex
+@misc{xu2022pidnet,
+      title={PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller},
+      author={Jiacong Xu and Zixiang Xiong and Shankar P. Bhattacharyya},
+      year={2022},
+      eprint={2206.02066},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

configs/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py

@@ -0,0 +1,10 @@
+_base_ = './pidnet-s_2xb6-120k_1024x1024-cityscapes.py'
+checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-l_imagenet1k_20230306-67889109.pth'  # noqa
+model = dict(
+    backbone=dict(
+        channels=64,
+        ppm_channels=112,
+        num_stem_blocks=3,
+        num_branch_blocks=4,
+        init_cfg=dict(checkpoint=checkpoint_file)),
+    decode_head=dict(in_channels=256, channels=256))

configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py

@@ -0,0 +1,5 @@
+_base_ = './pidnet-s_2xb6-120k_1024x1024-cityscapes.py'
+checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-m_imagenet1k_20230306-39893c52.pth'  # noqa
+model = dict(
+    backbone=dict(channels=64, init_cfg=dict(checkpoint=checkpoint_file)),
+    decode_head=dict(in_channels=256))

configs/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py

@@ -0,0 +1,113 @@
+_base_ = [
+    '../_base_/datasets/cityscapes_1024x1024.py',
+    '../_base_/default_runtime.py'
+]
+
+# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa
+# Licensed under the MIT License
+class_weight = [
+    0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786,
+    1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529,
+    1.0507
+]
+checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-s_imagenet1k_20230306-715e6273.pth'  # noqa
+crop_size = (1024, 1024)
+data_preprocessor = dict(
+    type='SegDataPreProcessor',
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255,
+    size=crop_size)
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    backbone=dict(
+        type='PIDNet',
+        in_channels=3,
+        channels=32,
+        ppm_channels=96,
+        num_stem_blocks=2,
+        num_branch_blocks=3,
+        align_corners=False,
+        norm_cfg=norm_cfg,
+        act_cfg=dict(type='ReLU', inplace=True),
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)),
+    decode_head=dict(
+        type='PIDHead',
+        in_channels=128,
+        channels=128,
+        num_classes=19,
+        norm_cfg=norm_cfg,
+        act_cfg=dict(type='ReLU', inplace=True),
+        align_corners=True,
+        loss_decode=[
+            dict(
+                type='CrossEntropyLoss',
+                use_sigmoid=False,
+                class_weight=class_weight,
+                loss_weight=0.4),
+            dict(
+                type='OhemCrossEntropy',
+                thres=0.9,
+                min_kept=131072,
+                class_weight=class_weight,
+                loss_weight=1.0),
+            dict(type='BoundaryLoss', loss_weight=20.0),
+            dict(
+                type='OhemCrossEntropy',
+                thres=0.9,
+                min_kept=131072,
+                class_weight=class_weight,
+                loss_weight=1.0)
+        ]),
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(
+        type='RandomResize',
+        scale=(2048, 1024),
+        ratio_range=(0.5, 2.0),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='GenerateEdge', edge_width=4),
+    dict(type='PackSegInputs')
+]
+train_dataloader = dict(batch_size=6, dataset=dict(pipeline=train_pipeline))
+
+iters = 120000
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None)
+# learning policy
+param_scheduler = [
+    dict(
+        type='PolyLR',
+        eta_min=0,
+        power=0.9,
+        begin=0,
+        end=iters,
+        by_epoch=False)
+]
+# training schedule for 120k
+train_cfg = dict(
+    type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(
+        type='CheckpointHook', by_epoch=False, interval=iters // 10),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='SegVisualizationHook'))
+
+randomness = dict(seed=304)

configs/pidnet/pidnet.yml

@@ -0,0 +1,81 @@
+Collections:
+- Name: PIDNet
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    URL: https://arxiv.org/pdf/2206.02066.pdf
+    Title: 'PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller'
+  README: configs/pidnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/pidnet.py
+    Version: dev-1.x
+  Converted From:
+    Code: https://github.com/XuJiacong/PIDNet
+Models:
+- Name: pidnet-s_2xb6-120k_1024x1024-cityscapes
+  In Collection: PIDNet
+  Metadata:
+    backbone: PIDNet-S
+    crop size: (1024,1024)
+    lr schd: 120000
+    inference time (ms/im):
+    - value: 12.37
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (1024,1024)
+    Training Memory (GB): 3.38
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.74
+      mIoU(ms+flip): 80.87
+  Config: configs/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700-bb8e3bcc.pth
+- Name: pidnet-m_2xb6-120k_1024x1024-cityscapes
+  In Collection: PIDNet
+  Metadata:
+    backbone: PIDNet-M
+    crop size: (1024,1024)
+    lr schd: 120000
+    inference time (ms/im):
+    - value: 13.89
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (1024,1024)
+    Training Memory (GB): 5.14
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.22
+      mIoU(ms+flip): 82.05
+  Config: configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452-f9bcdbf3.pth
+- Name: pidnet-l_2xb6-120k_1024x1024-cityscapes
+  In Collection: PIDNet
+  Metadata:
+    backbone: PIDNet-L
+    crop size: (1024,1024)
+    lr schd: 120000
+    inference time (ms/im):
+    - value: 16.65
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (1024,1024)
+    Training Memory (GB): 5.83
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.89
+      mIoU(ms+flip): 82.37
+  Config: configs/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514-0783ca6b.pth

mmseg/models/backbones/__init__.py

@@ -11,6 +11,7 @@
 from .mit import MixVisionTransformer
 from .mobilenet_v2 import MobileNetV2
 from .mobilenet_v3 import MobileNetV3
+from .pidnet import PIDNet
 from .resnest import ResNeSt
 from .resnet import ResNet, ResNetV1c, ResNetV1d
 from .resnext import ResNeXt
@@ -26,5 +27,5 @@
     'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3',
     'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer',
     'BiSeNetV1', 'BiSeNetV2', 'ICNet', 'TIMMBackbone', 'ERFNet', 'PCPVT',
-    'SVT', 'STDCNet', 'STDCContextPathNet', 'BEiT', 'MAE'
+    'SVT', 'STDCNet', 'STDCContextPathNet', 'BEiT', 'MAE', 'PIDNet'
 ]